ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Modeling a Shared Reality of Tractography through Varied Structural Imaging.bioRxiv : the preprint server for biology · 2026Article
- Generative Models for Medical Image Creation and Translation: A Scoping Review.Sensors (Basel, Switzerland) · 2026Article
- Modeling a Shared Reality of Tractography through Varied Structural Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Survey on sampling conditioned brain images and imaging measures with generative models.Biomedical engineering letters · 2025Review
- QIDComputational diffusion MRI. CDMRI (Workshop) · 2025Article
- Geometric deep learning for diffusion MRI signal reconstruction with continuous samplings (DISCUS).Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Diffusion MRI with Machine Learning.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Artificial intelligence for diffusion MRI-based tissue microstructure estimation in the human brain: an overview.Frontiers in neurology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Current deep learning approaches for diffusion MRI modeling circumvent the need for densely-sampled diffusion-weighted images (DWIs) by directly predicting microstructural indices from sparsely-sampled DWIs. However, they implicitly make unrealistic assumptions of static
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.